Education

• PhD in Quantum Physics from Stellenbosch University - 2015

• Thesis: Dynamics of Long-Range Quantum Spin Systems

• Applications:

  • Rapid information transfer in quantum systems
  • Spreading of correlations in quantum systems
  • Theory of trapped ion quantum simulators
  • Wishlist: Development of quantum computers

Most Cited Peer Reviewed Fundamental Research

• Breakdown of Quasilocality in Long-Range Quantum Lattice Models
  • J Eisert, M van den Worm, SR Manmana and M Kastner
  • Physical Review Letters 111 (26), 260401
  • Cited by 144
• Relaxation timescales and decay of correlations in a long-range interacting quantum simulator
  • M van den Worm, BC Sawyer, JJ Bollinger and M Mastner
  • New Journal of Physics 15, 083007
  • Cited by 105
• Quantum correlations and entanglement in far-from-equilibrium spin systems
  • KRA Hazzard, Mvan den Worm, et al.
  • Physical Review A 90 (6), 063622
  • Cited by 94

Citation History

Coauthor Network

Time at Polar Star

Corn Life Cycle

Corn Calendar Spread Example

Lean Hog Calendar Spread Example

RBOB Gasoline Curve Example

Curve Structure - Contango

Curve Structure - Backwardation

How the Curve Structure Affects Prices (1/4)

How the Curve Structure Affects Prices (2/4)

How the Curve Structure Affects Prices (3/4)

How the Curve Structure Affects Prices (4/4)

Backwardation and Annualised Return

Using the Curve in your favour

• Each commodity has a different curve structure
• You can think of the curve structure as a tide
• It is much easier to swim with the tide than against it
• For many commodities the curve structure has cyclical behaviour
• Using Curve Carry we try to exploit this
• Using the curve structure in a trend strategy helps in entering profitable long term trades

Fundamental Law of Portfolio Management

\[ \large \text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}} \]

• Skill measures your ability to find profitable opportunities
• Breadth captures how many opportunities you have available

Coin Flipping Casino (1/5)

• We know the coin is biased with \(P(\text{heads}) = 0.51\)
• We have 1000 coins
• The minimum wager is 1 coin
• If you win you gain 1 coin
• If you loose you loose 1 coin
• There are 1000 tables with coin wagers

Coin Flipping Casino (2/5)

How do we place the bets?

Two extremes:

• Bet 1000 coins on one coin flip
• Bet 1 coin on 1000 coin flips

Coin Flipping Casino (3/5)

The same expected return:

• Single bet: \(0.51 \times 1000 + 0.49 \times (-1000) = 20\)
• Multi bet: \(1000 \times [0.51 + 0.49 \times (-1)] = 20\)

Totally different risk to loose it all:

• Single bet: 49%
• Multi bet: \(0.49 \times 0.49 \times \dots \times 0.49 = 0.49^{1000} \approx 0\)

Coin Flipping Casino (4/5)

Another way to look at risk is to use standard deviation of returns

• Multi bet:

\[ \text{risk} := \text{std}\left\{1,-1,-1,1, \dots, 1 \right\} = 1 \]

• Single bet:

\[ \begin{align} \text{risk} &:= \text{std}\left\{1000,0,0,0, \dots, 0 \right\} = 31.62 \\ \text{risk} &:= \text{std}\left\{-1000,0,0,0, \dots, 0 \right\} = 31.62 \end{align} \]

Coin Flipping Casino (5/5)

Risk Adjusted Returns (Sharpe Ratio):

• Single bet: \(\text{SR}_{\text{single}} = \frac{20}{31.62} =0.63\)
• Multi bet: \(\text{SR}_{\text{multiple}} = \frac{20}{1} =20\)

We rewrite the risk adjusted return as:

• \(20 = 0.63 \times \sqrt{1000}\)
• \(\text{SR}_{\text{multiple}} = \text{SR}_{\text{single}} \times \sqrt{\text{Bets}}\)
• \(\text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}}\)

Three Pillars of Diversification

1. What you trade
   • Different commodities
   • Different calendar spreads
   • Different parts of the curve
2. How you trade
   • Trend
   • Curve Carry
3. When you trade
   • Different signals
   • Different times of the year
   • Roll methodology and timing

All of these add to the breadth of opportunities.

View of Current Book

Curve Carry (1/n)

Aim of the strategy:

• Harness the curve carry
• Storable commodities have predictable cuve structure
• Higher stock levels are associated with contango curves
• Many commodities evolve into a contango as the front nears expiry

We define \(p\) as the ratio of the Front to the Deferred price \[ p := \text{Front}/\text{Deferred} \]

• When \(p\) < 1 the Front is trading at a discount to the Deferred.
• The smaller \(p\) the greater the contango

Curve Carry (2/n)

Curve Carry (3/n)

Curve Carry (4/n)

Curve Carry (5/n)

Curve Carry (6/n)

1. From a fundamental point of view we

   • Identify commodity calendar spread pairs
   • With predictable behaviour during certain parts of the year

2. For every month of the year we have a list of

   • Commodities,
   • Associated calendar spread we want to be involved with and
   • The side of the trade

3. Meta-labeling: ML Technique to determine probability of the spread ending in the money

4. Size positions according to the

   • Volatility and
   • Probability of profit
   • Lower volatility receives a higher allocation

Curve Carry (7/n)

Curve Carry - Risk and Return Statistics (8/n)

Curve Carry - Time Windown Report (9/n)

Time Window Analysis:

Drawdown Report:

Trend Following (1/n)

Aim of the strategy:

• Trade commodity trends profitably
• Lookback windows ranging from a couple of weeks to one year
• Collection of signals:
  • Breakout,
  • Donchain and
  • Exponential Moving average signals
• Target volatility 20%
• Diverse universe of commodities
• Multiple parts of the futures curves
• Roll structure created from fundamental understanding

Essence: Cap your losses and let the winners run

Trend Following (2/n)

Build the strategy on fake data:

• Create data with trends
• See if we can trade it profitably
• Investigate price behaviour that cannot be traded profitably

Why is this preferred:

• No overfitting of the underlying market data
• Datamined lookbacks and models perform great in sample
• Majestic performance is difficult to follow up on new data

Trend Following (3/n)

Trend Following (4/n)

Trend Following (5/n)

Trend Following - Risk and Return Statistics (6/n)

Trend Following - Time Windown Report (7/n)

Time Window Analysis:

Drawdown Report:

Trend Following - Recent Years (8/n)

You Can’t Always Trend When You Want - AQR

Trend Following - Recent Years (9/n)

Breaking Bad Trends - Garg, Goulding, Harvey and Mazzoleni

Trend Following - Recent Years (10/n)

Return Scatterplot

Rolling Correlations

Return Distributions

Zig and Zag

Academic References

• Carry and Trend in Lots of Places - Bhansali, Davis, Dorsten and Rennison

• Tactical allocation in commodity futures markets: Combining momentum and term structure signals - Fuertes, Miffre and Rallis

Portfolio View

Portfolio View - Risk and Return Statistics

Portfolio View - Time Windown Report

Time Window Analysis:

Drawdown Report:

What we hope to achieve

• Long term positive returns
• Exploit fundamental knowledge and curve structure
• Diversified quantitative commodity product
• Low correlation to
  • other Polar Star funds,
  • equities and
  • commodity indices

Next meeting: On the stream…